The Existence-Dependency Graph

2014 ◽  
pp. 79-105
Author(s):  
Monique Snoeck
Keyword(s):  
Dependency Graph

scholarly journals UPR: deadlock-free dynamic network reconfiguration by exploiting channel dependency graph compatibility

2021 ◽  
Author(s):  
Juan-José Crespo ◽  
José L. Sánchez ◽  
Francisco J. Alfaro-Cortés ◽  
José Flich ◽  
José Duato
Keyword(s):  
Dynamic Network ◽  
Dependency Graph ◽  
Network Reconfiguration ◽  
Free Dynamic

Dependency Graph-based High-level Synthesis for Maximum Instruction Parallelism

2021 ◽  
Vol 14 (4) ◽  
pp. 1-15
Author(s):  
Zhenghua Gu ◽  
Wenqing Wan ◽  
Jundong Xie ◽  
Chang Wu
Keyword(s):  
Performance Optimization ◽  
Directed Acyclic Graph ◽  
Scheduling Algorithm ◽  
Dependency Graph ◽  
High Level Synthesis ◽  
Limiting Factor ◽  
Circuit Performance ◽  
State Transition Graph ◽  
High Level ◽  
Basic Blocks

Performance optimization is an important goal for High-level Synthesis (HLS). Existing HLS scheduling algorithms are all based on Control and Data Flow Graph (CDFG) and will schedule basic blocks in sequential order. Our study shows that the sequential scheduling order of basic blocks is a big limiting factor for achievable circuit performance. In this article, we propose a Dependency Graph (DG) with two important properties for scheduling. First, DG is a directed acyclic graph. Thus, no loop breaking heuristic is needed for scheduling. Second, DG can be used to identify the exact instruction parallelism. Our experiment shows that DG can lead to 76% instruction parallelism increase over CDFG. Based on DG, we propose a bottom-up scheduling algorithm to achieve much higher instruction parallelism than existing algorithms. Hierarchical state transition graph with guard conditions is proposed for efficient implementation of such high parallelism scheduling. Our experimental results show that our DG-based HLS algorithm can outperform the CDFG-based LegUp and the state-of-the-art industrial tool Vivado HLS by 2.88× and 1.29× on circuit latency, respectively.

scholarly journals Partially observable Markov Decision Process to prioritize software defects

2021 ◽  
Author(s):  
Shirin Akbarinasaji
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Tracking System ◽  
Dependency Graph ◽  
Relative Importance ◽  
Bug Reports ◽  
Markov Decision ◽  
Partially Observable Markov ◽  
Partially Observable ◽  
Issue Tracking System

Background: Bug tracking systems receive many bug reports daily. Although the software quality team aims to identify and resolve these bugs, they are never able to fix all of the reported bugs in the issue tracking system before the release deadline. However, postponing the bug fixing may have some consequences. Prioritization of bug reports will help the software manager decide which bugs to fix and which bugs to postpone. Typically, bug reports are prioritized based on the severity, priority, time and effort for fixing, customer pressure, etc. Aim: Previous studies have shown that these factors may not be appropriate for prioritization. Therefore, relying on them to automate bug prioritization might be misleading. In this dissertation, we aim to prioritize bug reports with respect to the consequence of not fixing the bugs in terms of their relative importance in the issue tracking system. Method: In order to measure the relative importance of bugs in the issue tracking system, we propose the construction of a dependency graph based on the reported dependency-blocking information in the issue tracking system. Two metrics, namely depth and degree, are used to measure the relative importance of the bugs. However, there is uncertainty in the dependency graph structure as the dependency information is discovered manually and gradually. Owing to this uncertainty, prioritization of bugs in the descending order of depth and degree may be misleading. To handle the uncertainty, we propose a novel approach of a partially observable Markov decision process (POMDP) and partially observable Monte Carlo planning (POMCP). Result: To check the feasibility of the proposed approach, we analyzed seven years of data from an open source project, Firefox, and a commercial project. We compared the proposed policy with the developer policy, maximum policy, and random policy. Conclusion: The results suggest that software practitioners do not consider the relative importance of bugs in their current practice. The proposed framework can be combined with practitioners’ expertise to prioritize bugs more effectively and take the depth and degree of bugs into account. In practice, the POMDP framework with the POMCP planner can help practitioners sequentially select bugs to minimize the connectivity of the dependency graph.

Generating Predictable and Adaptive Dialog Policies in Single- and Multi-domain Goal-oriented Dialog Systems

2021 ◽  
Vol 15 (04) ◽  
pp. 419-439
Author(s):  
Nhat Le ◽  
A. B. Siddique ◽  
Fuad Jamour ◽  
Samet Oymak ◽  
Vagelis Hristidis
Keyword(s):  
State Of The Art ◽  
Directed Graphs ◽  
Dependency Graph ◽  
The Other ◽  
Middle Ground ◽  
User Characteristics ◽  
Dialog Systems ◽  
Dependency Graphs ◽  
Novel Structure ◽  
Art Research

Most existing commercial goal-oriented chatbots are diagram-based; i.e. they follow a rigid dialog flow to fill the slot values needed to achieve a user’s goal. Diagram-based chatbots are predictable, thus their adoption in commercial settings; however, their lack of flexibility may cause many users to leave the conversation before achieving their goal. On the other hand, state-of-the-art research chatbots use Reinforcement Learning (RL) to generate flexible dialog policies. However, such chatbots can be unpredictable, may violate the intended business constraints, and require large training datasets to produce a mature policy. We propose a framework that achieves a middle ground between the diagram-based and RL-based chatbots: we constrain the space of possible chatbot responses using a novel structure, the chatbot dependency graph, and use RL to dynamically select the best valid responses. Dependency graphs are directed graphs that conveniently express a chatbot’s logic by defining the dependencies among slots: all valid dialog flows are encapsulated in one dependency graph. Our experiments in both single-domain and multi-domain settings show that our framework quickly adapts to user characteristics and achieves up to 23.77% improved success rate compared to a state-of-the-art RL model.

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